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Assessing the Performance of Classification Methods

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  • David J. Hand

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  • David J. Hand, 2012. "Assessing the Performance of Classification Methods," International Statistical Review, International Statistical Institute, vol. 80(3), pages 400-414, December.
    • Handle: RePEc:bla:istatr:v:80:y:2012:i:3:p:400-414
    DOI: j.1751-5823.2012.00183.x
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    File URL: http://hdl.handle.net/10.1111/j.1751-5823.2012.00183.x
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    Cited by:

    1. Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
    2. López-Díaz, María Concepción & López-Díaz, Miguel & Martínez-Fernández, Sergio, 2023. "On the optimal binary classifier with an application," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    3. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    4. Verme, Paolo & Gigliarano, Chiara, 2019. "Optimal targeting under budget constraints in a humanitarian context," World Development, Elsevier, vol. 119(C), pages 224-233.
    5. Bouvatier, Vincent & Lepetit, Laetitia & Rehault, Pierre-Nicolas & Strobel, Frank, 2023. "Time-varying Z-score measures for bank insolvency risk: Best practice," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 170-179.
    6. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    7. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    8. Vincent Bouvatier & Laetitia Lepetit & Pierre-Nicolas Rehault & Frank Strobel, 2018. "Bank insolvency risk and Z-score measures: caveats and best practice," Working Papers hal-01937929, HAL.
    9. David J. Hand, 2022. "Trustworthiness of statistical inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 329-347, January.
    10. D. J. Hand & C. Anagnostopoulos, 2023. "Notes on the H-measure of classifier performance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 109-124, March.
    11. Ilya Plyusnin & Liisa Holm & Petri Törönen, 2019. "Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-27, November.

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